User Churn Prediction Of Financial News APP

With the gradual transfer of PC traffic to the mobile end and the economic globalization increasing people 's demand for financial information,while the rapid growth of financial and economic APP users,the lack of user stickiness,the small proportion of loyal users,and the aggravation of user loss are the problems that financial and economic APP needs to solve urgently at present.If it is possible to accurately predict whether users will be lost soon,analyze the reasons for user churn before user churn,combine user related interests,and formulate corresponding strategies for each type of user with different values for user recovery.Identifying the users who are about to lose can realize the refined operation under the analysis of user data at the company's strategy level,and in terms of maintaining user traffic,in the face of the relatively large user base,it can reduce the pressure to acquire new users and maintain advantages in the user competition.Therefore,user churn prediction is the premise of improving user retention,as well as the key link of user growth and user management.Based on the basic data of financial news APP users and the behavior data generated by users in the APP,this paper proposes the process of predicting the loss of financial news APP users.There are three main research contents.The main research contents are as follows: the first part,extract the characteristic variables of financial news app user churn prediction.Define the loss of users of the financial news app,analyze the characteristics of the consumption mode and business model of the financial news app,combine the concepts of the RFM model and time window,process the basic user data and user behavior log data of the financial news app,construct and filter the financial news app Characteristic variables of user churn prediction.In the second part,the improved Stacking algorithm is used to predict the churn of financial news APP users.Obtain a prediction model of financial news APP user churn with higher prediction accuracy.The third part is the empirical research on the prediction of user churn of financial news APP.Empirical research on financial news APP user churn prediction based on S Finance News app user data,and verify the effectiveness of the model.Clarified the position of the financial news APP user churn prediction in the refined operation process of financial news APP users.In this article,we discuss the direction of application of financial news APP user churn prediction results.Through comparative experiments,it can be proved that the improved Stacking model proposed in this paper is better than the classification of random forest,SVM,Ada Boost and ordinary Stacking integration algorithms.In the empirical part of this paper,the follow-up application of user churn prediction results is discussed,which is to make an analysis on user recovery,which is of positive significance for solving practical problems.